Fuzzy logic and neural nets in intelligent systems∗

Hybrid intelligent systems combining fuzzy logic and neural networks are proving their effectiveness in a wide variety of real-world problems. Fuzzy logic and neural nets have particular computational properties that make them suited for particular problems and not for others. For example, while neural networks are good at recognizing patterns, they are not good at explaining how they reach their decisions. Fuzzy logic systems, which can reason with imprecise information, are good at explaining their decisions but they cannot automatically acquire the rules they use to make those decisions. These limitations have been a central driving force behind the creation of intelligent hybrid systems where these two techniques are combined in a manner that overcomes the limitations of individual techniques. The main goals of this paper are to explain the basic characteristics of fuzzy logic, neural nets and neuro-fuzzy systems; and to illustrate their (inference and learning) mechanism by the simplified fuzzy reasoning scheme.